No. SleekRank reads from your data source. If your sheet pulls APYs via a script or formula on a schedule, those flow through on the cache cycle. SleekRank does not scrape bank pages or call APIs directly. The pattern is a separate import job that updates the sheet, and SleekRank renders whatever is current in the source after a cache flush.

Both page groups read from the same accounts sheet. The pairs page group joins two rows at render time using the slug pair from a pairs sheet. A row edit propagates to wherever the account is referenced after the cache cycle, including every pair page where the account is product_a or product_b. The data layer enforces consistency that manual page editing cannot.

Use the tier_breakpoints array to compute an effective APY at common balance levels, then generate per-balance landing pages at /savings/by-balance/{slug}/ that filter and rank accounts at that level. Each balance-level page reads the same accounts sheet with a different ranking projection.

No. The verdict is whatever is written in the sheet. SleekRank does not write content, it injects content. For longer verdicts that exceed a sheet's column comfort, store them in a separate JSON file keyed by account slug and join at render time. The verdict text is yours, the render layer is the responsibility of SleekRank.

Yes. Map an image URL column to og:image with the meta type. Each account page can render a custom social card via that mapping. For dynamic per-account OG images that overlay current APY, fee status, and minimum balance over a styled background, pair with SleekPixel which renders OG images from data on the fly.

Update the tier_breakpoints JSON array in the sheet. Every page that references the account, including per-account, every pair page where it appears, and any balance-level roll-up, reflects the change after the cache window. Tier restructures are common and consequential, and this is exactly the kind of edit manual reviews fail to propagate.

Store signup_bonus, bonus_requirements, and bonus_expiration as columns. Render them in a promo callout via tag mapping on the per-account page, and join on pair pages to compare current bonuses side by side. When a promo expires, edit the row and every page where the account is referenced reflects it on the next cache cycle.

Yes. Store a time series of historical APYs in a separate JSON file keyed by account slug and render it as a small chart via a chart library on the base template. The page reflects current APY from the main row plus historical context from the side dataset, joined at render time, which helps readers judge whether a bank has been a consistent top payer or a temporary leader.